Cell fate determination is a key question in biology. Our group is interested in the molecular mechanisms controlling cell fate decisions during development (e.g., C. elegans vulval development), lineage commitment and differentiation in self-regenerating systems, and stem cell maintenance (e.g., the hematopoietic system, skin, and germ cell proliferation and apoptosis in C. elegans). Cell fate specification processes must be under strict regulation to ensure life-long homeostasis. The signalling pathways that are known to play a key role in development and self-regenerating tissues - such as Ras, Notch and Wnt - are often deregulated during tumorigenesis and cancer due to mutations in key elements of these pathways. A better understanding of the mechanisms controlling cell fate decisions can pave the way to the identification of novel drug targets and improve potential strategies to fight tumorigenesis and cancer. However, due to their enormous complexity and multiple interactions, the comprehension and analysis of these signalling pathways have been a major challenge.
Over the last decade at Microsoft Research, our group has been pioneering the use of program analysis techniques for the construction and analysis of executable models describing cell fate decisions in various model systems (e.g., C. elegans, Drosophila, S. cerevisiae, mammalian epidermis, and blood cells). These models are essentially computer programs whose behaviour captures aspects of biological phenomena. Over the years, these efforts have demonstrated successfully how the use of formal methods can be beneficial for gaining new biological insights and directing new experimental avenues. Once an executable model has been built, it can be used to get a global dynamic picture of how the system responds to various perturbations. In addition, preliminary studies (in silico) can be quickly performed on a computational model, saving valuable laboratory time and resources for only the most promising avenues. By applying various computational approaches ranging from Boolean/qualitative networks, compositional state-machines, Petri nets, to process calculi, our group devotes itself both to establishing an in-depth understanding of the cell signalling and intercellular communication processes controlling cell fate specification, and to understanding how these processes are orchestrated to establish robust cell fate patterns. By developing methods in model checking and synthesis we have discovered mechanisms of cell fate determination and found new approaches to derive models directly from classical mutation experiments and high throughput data.
Lab members: Steven Woodhouse, Matthew Clarke, Chee Yee Lim, Victoria Wang, Dana Silverbush, Erin Oerton, Diana Danciu
Chuang R., Benque D., Cook B., Hall B.A., Ishtiaq S., Piterman N., Taylor A., Vardi M., Koschmieder S., Gottgens B. and Fisher J. Drug Target Optimization in Chronic Myeloid Leukemia Using an Innovative Computational Platform. Scientific Reports, 5:8190, 2015.
Moignard V., Woodhouse S., Haghverdi L., Lilly J, Tanaka Y, Wilkinson A.C, Buettner F., Nishikawa S.I., Piterman N., Kouskoff V., Theis F.J., Fisher J., Göttgens B. Decoding the Transcriptional Program for Blood Development from Whole Tissue Single Cell Gene Expression Measurements. Nature Biotechnology, 2015.
Nusser-Stein S., Beyer A., Rimann I., Adamczyk M., Piterman N., Hajnal A., and Fisher J., Cell-Cycle Regulation of Notch Signaling during C. elegans Vulval Development, in Molecular Systems Biology, 8:618, 2012.
Benque D., Bourton S., Cockerton C., Cook B., Fisher J., Ishtiaq S., Piterman N., Taylor A., and Vardi M., Bio Model Analyzer: Visual Tool for Modeling and Analysis of Biological Networks, in Computer Aided Verification (CAV) 2012. LNCS 7358, pp. 686-692 , Springer Verlag, July 2012.
Bonzanni N., Zhang N., Oliver S.G., and Fisher J., The role of proteosome-mediated proteolysis in modulating potentially harmful transcription factor activity in S. cerevisiae, in Bioinformatics, Vol. 27, pages i283-i287, 2011.
Schaub M.A., Henzinger, T.A., & Fisher J. Qualitative Networks: A Symbolic Approach to Analyze Biological Signaling Networks. BMC Systems Biology. 1:4, 2007.
Wang D., Cardelli L., Phillips A., Piterman N., and Fisher J., Computational Modeling of the EGFR Network Elucidates Control Mechanisms Regulating Signal Dynamics, in BMC Systems Biology 3:118, 22, 2009.
Fisher J., Piterman N., Hajnal A., & Henzinger, T.A. Predictive Modeling of Signaling Crosstalk during C. elegans Vulval Development. PLoS Computational Biology. 3(5):e92, 2007.
Fisher J. and Henzinger T.A., Executable Cell Biology, in Nature Biotechnology, vol. 25, no. 11, pp. 1239-1249, 2007.
Fisher J., Piterman N., Hubbard J., Stern M., & Harel D. Computational insights into C. elegans vulval development. PNAS 102(6):1951-1956, 2005.